Department of Biomedical Engineering, School of electrical engineering, Iran Neural Technology Research Center, Iran University of Science and Technology (IUST), Tehran, Iran.
Sci Rep. 2018 Jan 12;8(1):577. doi: 10.1038/s41598-017-18971-x.
Decoding continuous hind limb joint angles from sensory recordings of neural system provides a feedback for closed-loop control of hind limb movement using functional electrical stimulation. So far, many attempts have been done to extract sensory information from dorsal root ganglia and sensory nerves. In this work, we examine decoding joint angles trajectories from the single-electrode extracellular recording of dorsal horn gray matter of the spinal cord during passive limb movement in anesthetized cats. In this study, a processing framework based on ensemble learning approach is propose to combine firing rate (FR) and interspike interval (ISI) information of the neuronal activity. For this purpose, a stacked generalization approach based on recurrent neural network is proposed to enhance decoding accuracy of the movement kinematics. The results show that the high precision neural decoding of limb movement can be achieved even with a single electrode implanted in the spinal cord gray matter.
从神经系统的感觉记录中解码连续的后肢关节角度，为使用功能性电刺激对后肢运动进行闭环控制提供了反馈。到目前为止，已经有许多尝试从背根神经节和感觉神经中提取感觉信息。在这项工作中，我们检查了在麻醉猫的被动肢体运动期间，从脊髓背角灰质的单电极细胞外记录中解码关节角度轨迹。在这项研究中，提出了一种基于集成学习方法的处理框架，以结合神经元活动的发放率 (FR) 和峰间间隔 (ISI) 信息。为此，提出了一种基于递归神经网络的堆叠泛化方法来提高运动运动学的解码精度。结果表明，即使在后肢运动中植入单个电极，也可以实现高精度的神经解码。
